An additional constraint comes from the difficulty of characterizing the toxicity of the social media content (See REF ) on which the RS must act. This results both in stochastic actions' results (e.g. content that was mistakenly supposed to be toxic undergoes reduced propagation speed) and stochastic rewards (toxic content is evaluated by error as healthy and a positive reward is provided to the CWBRS). These, and especially the latter, are indeed interesting issues to study. While the RL method accounts for noisy actions' results, they still affect the performance of the system, both in terms of execution and learning time. Regarding noisy reward, literature has only recently started to provide solutions [1]}, [2]}. Still, it must be noted that in our setting, getting a positive reward for something that was considered positive should not crucially impair the acquired RS policy as the system allowed the propagation of something that it evaluated healthy (or toxic) and accordingly evaluated its reception by other SM users. Thus, in this case, the two errors may cancel each-other out and take advantage of improvements in the detectors. Moreover, when applying RL for ITS, an additional strategy that can be leveraged to counter these issues is to use more reliable tests that would allow to evaluate the state of the users and provide more reliable, but sparse reward. Indeed, the problem of noisy rewards and actions' results will be crucial for the growing field of deep reinforcement learning based ITS [3]}, [4]}.
Due to social media complexities, the effects of detectors' failures on the performance of CWBRS can be unexpected, with backfiring as the worse case scenario. We plan to extensively study them in simulation before (e.g. [5]} and with group studies later, and compare the results with those of classical recommender systems for social media that are not sensitive to content toxicity.
